Metadata-Version: 2.4
Name: dcTMD
Version: 0.4.1
Summary: Analyse targeted molecular dynamics data with dcTMD
Home-page: https://github.com/moldyn/dcTMD
Author: taenzel, dieJaegerIn, braniii, floWneffetS
License: MIT License
Project-URL: Documentation, https://moldyn.github.io/dcTMD
Project-URL: Source Code, https://github.com/moldyn/dcTMD
Project-URL: Bug Tracker, https://github.com/moldyn/dcTMD/issues
Keywords: enhanced sampling,friction,MD analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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  <p>
    <a href="#features">Features</a> •
    <a href="#installation">Installation</a> •
    <a href="https://moldyn.github.io/dcTMD/getting_started/">Tutorials</a> •
    <a href="https://moldyn.github.io/dcTMD/">Docs</a>
  </p>
</div>


# dcTMD

This package aids in the analysis of dissipation-corrected targeted molecular dynamics (dcTMD) simulations. The method enforces rare unbinding events of ligands from proteins via a constraint pulling bias. Subsequently, free energy profiles and friction factors are estimated along the unbinding coordinate. For a methodological overview, see our [article](https://pubs.acs.org/doi/full/10.1021/acs.jctc.8b00835).

> S. Wolf, and G. Stock,  
> *Targeted molecular dynamics calculations of free energy profiles using a nonequilibrium friction correction.*,  
> **J. Chem. Theory Comput.** 2018 14 (12), 6175-6182,  
> doi: [10.1021/acs.jctc.8b00835](https://pubs.acs.org/doi/10.1021/acs.jctc.8b00835)

This package will be published soon:

> M. Jäger, V. Tänzel, D. Nagel, and S. Wolf,  
> *Dissipation Corrected Targeted Molecular Dynamics*,    
> in preparation 2025

We kindly ask you to cite these articles in case you use this software package for published works.

## Features
- Intuitive usage via module and CI
- Sklearn-style API for fast integration into your Python workflow
- Supports Python 3.9-3.14
- Multitude of [publications](https://www.moldyn.uni-freiburg.de/publications.html) with dcTMD

## Implemented Key Functionalities
- Estimation of free energy profiles and friction factors along the unbinding coordinate of ligands as described by [Wolf and Stock 2018](https://pubs.acs.org/doi/full/10.1021/acs.jctc.8b00835).
- Analysis of separate unbinding pathways as described by [Wolf et al. 2023](https://pubs.aip.org/aip/jcp/article/158/12/124106/2881566/Path-separation-of-dissipation-corrected-targeted).

## Installation
`dcTMD` is available on **PyPI** and **conda-forge**.
#### Install with pip
```bash
pip install dcTMD
```
PyPI project page: [https://pypi.org/project/dcTMD/](https://pypi.org/project/dcTMD/)
#### Install with conda
```bash
conda install conda-forge::dctmd
```
Conda-forge package page:
[https://anaconda.org/conda-forge/dcTMD](https://anaconda.org/conda-forge/dcTMD)
#### Install from GitHub
```bash
python3 -m pip install git+ssh://git@github.com/moldyn/dcTMD.git
```

## Usage
Check out the documentation for an overview over all modules as well as the tutorials.



